Integrated Bioinformatics Algorithms and Experimental Validation to Explore Robust Biomarkers and Landscape of Immune Cell Infiltration in Dilated Cardiomyopathy.

Background: The etiology of dilated cardiomyopathy (DCM) is unclear. Bioinformatics algorithms may help to explore the underlying mechanisms. Therefore, we aimed to screen diagnostic biomarkers and identify the landscape of immune infiltration in DCM.

Methods: First, the CIBERSORT algorithm was used to excavate the proportion of immune-infiltration cells in DCM and normal myocardial tissues. Meanwhile, the Pearson analysis and principal component analysis (PCA) were used to identify immune heterogeneity in different tissues. The Wilcoxon test, LASSO regression, and machine learning method were conducted to identify the hub immune cells. In addition, the differentially expressed genes (DEGs) were screened by the limma package, and DEGs were analyzed for functional enrichment. In the protein-protein interaction (PPI) network, multiple algorithms were used to calculate the score of each DEG for screening the hub genes. Subsequently, external datasets were used to further validate the expression of hub genes, and the receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy. Finally, we examined the expression of hub biomarkers in animal models.

Results: A total of 108 DEGs were screened, and these genes may be related to biological processes such as cytolysis, positive regulation of cytokine secretion, etc. Two types of hub immune cells [activated natural killer (NK) cells and eosinophils] and four hub genes (, and ) were identified in DCM myocardial tissues. was verified to have the capability to diagnose DCM with the most excellent specificity and sensitivity. It is worth mentioning that the combined and eosinophils may have better diagnostic efficacy. Moreover, the correlation analysis showed was negatively correlated with activated NK cells. Finally, the results of the mice model also indicated that might be involved in the occurrence of DCM.

Conclusion: , and may have a potential predictive ability for DCM, and especially showed the most robust efficacy. Furthermore, activated NK cells and eosinophils may relate to the occurrence of DCM.